Frops Analytics - Machine Learning and AI in the Detection, Prevention, and Litigation of Fraud in insurance Qorus-NTT DATA Innovation in Insurance Awards 2025- Nominated

FROPS (Fraud Risk Operational Performance System) is an innovative solution in the fight against insurance fraud, using Machine Learning and Artificial Intelligence to identify suspicious patterns in real-time and adapt to various situations. The platform is designed to efficiently monitor and analyze large volumes of data, preventing fraud attempts before they occur.

The central objective of FROPS is to significantly reduce insurance fraud by providing an automated tool that identifies suspicious activities with high accuracy. This is made possible through the implementation of ML and AI algorithms that analyze extensive data sets and recognize anomalous or fraudulent behaviors.

The solution enhances existing processes in combating fraud and incorporates new Machine Learning and Artificial Intelligence capabilities, making detection more efficient and automated. FROPS provides a variety of essential features for combating fraud, such as:

Automatic Fraud Detection

Machine Learning and Artificial Intelligence Technologies

Real-Time Analysis

Adaptation to New Fraud Patterns

Integration with External Databases

Predictive Analysis

Risk Scoring System

Monitoring Dashboards

Predictive models for evaluating the probability of litigation and process outcomes

Insurance fraud represents a global challenge that generates significant financial losses for insurers and customers. It is estimated that fraud losses in the insurance sector amount to billions of dollars annually, directly impacting policy costs and the sustainability of the sector. Besides financial losses, fraud undermines consumer trust in insurers, reinforcing the need for effective technological solutions that can proactively and efficiently mitigate these risks.